Network design utilizing network management routing algorithm

ABSTRACT

Techniques for designing networks. The techniques utilize network management-based routing (NMS routing) in conjunction with the planning step (design-based routing) of the design process so that an optimal network may be designed. An automated technique for designing a network may comprise the following steps. First, one or more traffic demands are obtained. Then, a network is computed by determining one or more routes for the one or more traffic demands using a design-based routing methodology based on feedback from a network management-based routing methodology.

CROSS REFERENCE TO RELATED APPLICATION

This application relates to the U.S. patent application identified asSer. No. 10/426,500, entitled “Network Design Utilizing IntegratedPlanning and Configuration,” filed concurrently herewith and commonlyassigned, the disclosure of which is incorporated by reference herein.

FIELD OF THE INVENTION

The present invention relates to techniques for designing networks and,more particularly, to network design-techniques utilizing a networkmanagement routing algorithm.

BACKGROUND OF THE INVENTION

Network design tools are offline tools used to plan and deploy networks,such as optical networks. A central goal of any network design tool isto come up with an optimal network that can carry the given set oftraffic demands. The design process involves routing the demands subjectto one of several optimization metrics, such as the dollar cost of thenetwork or the path lengths of the demands. The resulting design is usedin multiple ways. Equipment vendors use the design in responding torequests for proposal (RFP). Carriers use the design to deploy theirnetwork and conduct what-if analyses.

In the latter case, once the network has been deployed according to thedesign, an online system known as a network management system (NMS) isresponsible for routing the demands as they arrive in time. Typically,point-and-click software is used to automate this process over meshnetworks. The NMS software is typically responsible for computing theoptimal routes for the demands and signaling the cross connects tocorrectly set up the lightpaths. One example of NMS software is aproduct called WaveStar™ SNMS available from Lucent Technologies, Inc.(Murray Hill, N.J.).

Clearly, it is expected that the design network is operationallyeffective, i.e., is able to successfully carry the traffic for which itwas designed. While this may seem like a foregone conclusion, there arecertain disparities or mismatches between the design phase (offlineroute planning) and operation phase (online NMS-based routing) that makethis difficult to achieve in practice.

First, the optimization criteria used to route demands during the twophases are often unrelated. For example, the design algorithm may berouting to minimize costs, while the NMS may be routing to minimizenetwork congestion. As a result, the design algorithm may allocatecapacity for a demand on certain links, whereas the NMS is trying toroute the demand on different links. In some cases, this may result innot being able to route a demand.

Second, the order in which the demands arrive may be different from theorder assumed during design algorithms. Since routing typically dependson the current usage of the network (e.g., in congestion-based routing),the computed paths may once again be different in design and operations,adding to the above problem. Clearly, failure to route a demand whenthere is stranded capacity in the network can be catastrophic to thebottom line of a network carrier. The solution adopted in practice is toforce the NMS to route the demands along the paths computed by thedesign algorithm. However, this is not satisfactory since it ignores anyoperational criteria in routing.

SUMMARY OF THE INVENTION

The present invention provides automated techniques for designingnetworks. The techniques utilize network management-based routing (NMSrouting) in conjunction with the planning step (design-based routing) ofthe design process so that an optimal network may be designed.

In one aspect of the invention, an automated technique for designing anetwork comprises the following steps. First, one or more trafficdemands are obtained. Then, a network is computed by determining one ormore routes for the one or more traffic demands using a design-basedrouting methodology based on feedback from a network management-basedrouting methodology.

The network computing step may comprise the following steps. First, theone or more traffic demands may be routed using the design-based routingmethodology to determine an initially-designed network topology. Then,the one or more traffic demands may be routed on the initially-designednetwork topology using the network management-based routing methodologyto determine whether there is an unroutable traffic demand. When thereis an unroutable traffic demand, at least that demand may be reroutedusing the design-based routing methodology to determine a revisednetwork topology. The network management-based routing methodology maythen be run again on the revised network. The network design techniquemay be performed iteratively until all unroutable traffic demands arererouted. The process yields a designed network.

Advantageously, by executing a design-based routing methodology (oralgorithm) in accordance with feedback from a network management-basedrouting methodology (or algorithm), the techniques of the inventionprovide an optimal network design that is able to account forpotentially disparate optimization metrics that may be employed by thetwo algorithms.

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an example of an optical network;

FIG. 2 is a block diagram illustrating an automated design system,according to an embodiment of the present invention;

FIG. 3 is a flow diagram illustrating an automated design methodology,according to an embodiment of the present invention; and

FIG. 4 is a block diagram illustrating a generalized hardwarearchitecture of a computer system suitable for implementing an automateddesign system, according to an embodiment of the present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The following description will illustrate the invention in the contextof an exemplary optical network. It should be understood, however, thatthe invention is not necessarily limited to use with any particular typeof network. The invention is instead more generally applicable to anyenvironment in which it is desirable to design an optimal network. Thus,by way of example only, the techniques of the invention may also beapplied to wireless networks, Internet Protocol (IP) networks, etc. Itis to be appreciated that the term “cost,” as used herein, is notintended to be limited only to monetary cost, but rather the term isintended to refer more generally to the expenditure of something for theattainment of a goal.

In order to eliminate the above-described mismatch of the routingalgorithms used in planning (offline phase) and in NMS (online phase),the present invention provides a new planning and design algorithm whichutilizes an NMS routing algorithm in a feedback loop and designs arobust network.

In accordance with illustrative principles of the invention, a networkmay be designed using a routing algorithm that minimizes the dollar costof the network. Demands may then be routed on this network in a way thatthe NMS would route the demands in an online fashion. Typically, a fewof these demands would fail to be routed due to lack of sufficientnetwork capacity. Additional capacity may be added, in a cost optimalway, to the original designed network, for the demands which failed tobe routed. All demands may then be routed again on this newly designednetwork. This iteration may be continued until all demands aresuccessfully routed by the NMS routing algorithm on a network designedby the planning algorithm.

Integration of an NMS routing algorithm and a routing algorithm used todesign networks results in a robust optical network which minimizesrouting failure due to lack of capacity. The concept of having afeedback between NMS routing and planning routing algorithms can beapplied to any planning routing algorithm and any NMS routing algorithmcombination. The concept of linking these algorithms together during thedesign process provides a significant improvement in the design process.

The following portion of the detailed description formulates the genericnetwork design problem and then presents a set of design and NMSalgorithms. Let NW be a network topology comprising a set of locations(nodes) and rights-of-way (links). The nodes can contain cross connectelements, whereas the links can contain fiber and transmissionequipment. Let T be an ordered-set of point-to-point traffic demandsbetween the nodes. Each traffic demand, tεT, requires certain OC-nbandwidth and can be unprotected or 1+1 protected. An unprotectedtraffic demand is susceptible to a single network failure. However, atraffic with 1+1 protection can handle a single network failure using abackup path. Let ε be a set of equipment available, containing crossconnects and transmission system elements. A design problem instance canbe defined to be the tuple (NW, T), where NW may already contain someequipment and traffic.

Then, the generic network design problem can be stated as follows: Givena design problem instance (NW, T), place the equipment while minimizingsome optimization metric such that all the given traffic T can besuccessfully carried using the designed network.

The optimization metric is typically the dollar cost of the network, inwhich case, the metric leads to the cheapest network. However, it isalso possible to be some other metric, such as the path lengths of thedemands. In the latter case, the demands will be routed on theirshortest paths over the given rights-of-way.

The routing problem arising in the NMS phase, as well as in the designphase, can be stated as follows: Given a network NW possibly carryingsome traffic, and a new traffic demand t, compute a path from its sourceto destination and also any backup paths as required, subject to someoptimization metric.

It is assumed that if there is at least one path between the source anddestination with sufficient capacity, the routing algorithm will alwaysfind a path for the demand. Once again, the metric dictates how thepaths are computed. It is simplest to route the demands along theshortest paths. Consider a common alternative which tries to minimizethe congestion in the network. Congestion is a measure of the maximumlink-utilization in the network. The idea behind this criterion is to:(a) reduce the chance of failure to compute a path in the future due toinsufficient capacity; and (b) minimize the number of simultaneousdemands failing in case of a single link failure.

Next, a set of design algorithms that may be employed in accordance withthe invention are presented. The basic outline of the design algorithmsis as follows:

Step 1 (Routing): Compute paths for each demand. This may either be adirect operation involving routing each demand or it may involve anoptimization algorithm (e.g., linear program formulation) that resultsin routes for the demands.

Step 2 (Configuration): For each node and link, select the least-costequipment that can support the traffic going through it.

It is to be appreciated that the present invention is not limited to usewith any particular design algorithm or network management algorithm.Nonetheless, three examples of design algorithms that may be employedare described below.

Minimum Distance (MinDist): In this approach, demands are routed alongtheir shortest paths. This simple approach is quite useful in practicebecause many carriers feel comfortable with such intuitive routes fordemands. The shortest paths are computed using Dijkstra's algorithm,see, e.g., T. Cormen et al, “Introduction to Algorithms,” MIT Press,1990. To route 1+1 protected demands, one has to find two node-disjointpaths from source to destination. This problem is equivalent to findinga minimum cost 2-flow in a unit capacity network and the solutioninvolves applying Dijkstra's algorithm twice on a transformed network,see, e.g., J. W. Suurbaale, “Disjoint Paths in a Network,” Networks,vol. 4, June 1974.

Minimum Cost (MinCost): This algorithm attempts to minimize the dollarcost of the network. An example of such an algorithm is described in R.D. Davis et al., “Spider: A Simple and Flexible Tool for Design andProvisioning of Protected Lightpaths in Optical Networks,” Bell LabsTechnical Journal, January-June 2001, the disclosure of which isincorporated by reference herein. First, the demands are ordered in somerandom manner and each demand is routed on the cheapest path. Thecheapest path is computed using Dijkstra's shortest path algorithm asabove, but with the weights on the links being the dollar costs of thelinks if they were to carry the demand. Next, each demand is unrouted,freeing up resources allocated for that demand, and is rerouted. Thisprocess is repeated as long as the dollar cost of the overall networkkeeps decreasing.

Spanner: This algorithm only supports unprotected traffic, but gives aguarantee on the optimality of the solution. A k-spanner is defined asthe subgraph of the original graph G in which the maximum length of ashortest path is within k-times the maximum length of a shortest path inG, see, e.g., Y. Mansour et al., “An Approximation Algorithm forMinimum-Cost Network Design,” DIMACS Workshop on Robust CommunicationNetworks, 1998. The Spanner algorithm, finds a log(n) spanner, of thenetwork, where n is the nodes in the graph. The algorithm then appliesMinDist to route the demands in the spanner, see, e.g., M. Alicherry etal., “Designing Deployable Optical Networks,” Bell Labs TechnicalReport, 2002, the disclosure of which is incorporated by referenceherein. The idea is that the spanner is much sparser than the originalgraph, leading to a faster solution. An approximate log(n) spanner iscomputed using a greedy approach, which is an extension of Kruskal'salgorithm (see T. Cormen et al., “Introduction to Algorithms,” MITPress, 1990) for finding the minimal spanning tree. It has been shownthat the resulting network is within log(n) factor of the optimalnetwork that can be designed for the given set of demands.

It is also known to use other metrics or a combination of the metrics inpractice.

The following portion of the detailed description identifies theoperational problems arising in managing a designed network in thepresence of mismatches between the routing algorithms arising in designand NMS. It is easy to see that if the two routing algorithms usedifferent criteria for routing, they can lead to different paths for thesame demand. For example, the congestion-based approach tries to routethe demands over paths that are relatively unused. In contrast, thecost-based approach tries to reuse the same links because of the highstartup costs of laying a new transmission system. In certain cases,this will actually lead to insufficient capacity to route a demand, asshown in the following example.

Referring initially to FIG. 1, an example of an optical network isillustrated. Assume that the demand set comprise three OC-192 demandst_(1,8), t_(2,9), and t_(3,10) between the node pairs in the subscripts.In a MinCost design, the first demand will take N1-N4-N5-N8. The secondwill take N2-N4-N5-N9 because it is cheaper than laying a newtransmission system in the alternative path. Finally, the third demandtakes N3-N6-N7-N10. Now consider congestion-based NMS routing. The firstdemand will still take the same path, however, the second demand willnow take N2-N6-N7-N9 to spread out the load. Since capacity sufficientfor only one demand was provisioned over N6-N7, the third demand can notbe routed anymore.

The probability of failure to route depends on the correlation betweenthe two routing criteria. If the criteria are the same, all demands canbe routed over the designed network under the assumption that thedemands arrive in the same order. On the other hand, if they optimizecontradictory features (e.g., congestion versus cost), there is a muchhigher chance of failure. Of course, there can be a wide range ofunquantifiable correlations between two arbitrary routing criteria.

Accordingly, a central problem addressed by the present invention cannow be formulated as follows: Consider a network design problem (NW, T)solved using a design algorithm D. Let the routing algorithm used in theNMS be R which is different from the routing algorithm in D. Then,modify the designed network such that all the traffic demands in T canbe successfully routed using R (in the order they are given in T).

The following portion of the detailed description provides anillustrative system and methodology that are capable of producingoperationally viable designs for any combinations of design and NMSrouting algorithms.

Referring now to FIG. 2, a block diagram illustrates an automated designsystem, according to an embodiment of the present invention. As shown,design inputs 202 are provided by a user to design system 204 which,itself, comprises a planning engine 206 and a network management system(NMS) engine 208. The inputs to the design system may comprise a set ofnodes and a set of links or right-of-ways (the nodes and linksrepresenting an initial topology), as well as a set of traffic demands.

Design system 204 then computes a network 210 by determining one or moreroutes for the traffic demands using a design-based routing algorithmexecuted by the planning engine 206 based on feedback from a networkmanagement-based routing algorithm executed by the NMS engine 208. Thus,as will be further explained below, the planning engine 206 and the NMSengine 208 integrally operate in a feedback arrangement so as to yield adesigned network that substantially satisfies optimization goalsassociated with both the network design phase (e.g., minimize cost, pathlength) and the NMS routing phase (e.g., minimize congestion).

Referring now to FIG. 3, a flow diagram illustrates an automated designmethodology, according to an embodiment of the present invention. It isto be appreciated that methodology 300 shown in FIG. 3 may beimplemented, for example, by planning engine 206 and NMS engine 208 ofdesign system 204 of FIG. 2.

In step 302, a user inputs an input network topology NW including nodesand right-of-ways. Traffic demands T are also inputted. In step 304, anetwork is initially designed using a chosen design algorithm D. This isaccomplished by the planning engine 206 executing the chosendesign-based routing algorithm. It is to be appreciated that the designalgorithm may be one of the design algorithms described above (MinDist,MinCost, Spanner). However, other design algorithms may be employedsince the invention is not limited to any particular design algorithm.The result is a new network NW (initially-designed network) that handlesthe input traffic demands (block 306).

In step 308, all of the demands in the input traffic set T are thenrouted on this network using a chosen NMS routing algorithm R. This isaccomplished by the NMS engine 208 executing the chosen NMS routingalgorithm R. It is to be appreciated that the NMS routing algorithmutilized may be any known NMS-based algorithm. Examples of suchalgorithms include, but are not limited to, MinDist (as explained above)and minimum congestion-based algorithms. Such minimum congestion-basedalgorithms typically use the Dijkstra's shortest path algorithm (see T.Cormen et al., “Introduction to Algorithms,” MIT Press, 1990) where theweights used on the links to calculate the shortest path represent ameasure of congestion on the link. Hence, the path discovered is the onewith the minimum congestion. Also, the NMS routing algorithm utilizedcould be one of the routing algorithms available in the product referredto as WaveStar™ SNMS available from Lucent Technologies, Inc. (MurrayHill, N.J.). However, it is to be understood that other NMS routingalgorithms may be employed since the invention is not limited to anyparticular network management-based routing algorithm.

If any demands fail to be routed, denoted as unroutable demands T′ inblock 310, the network design algorithm is reinvoked (step 304),starting with the network designed so far as the initial network and thefailed demands as the new set of demands to be satisfied. Next, all ofthe demands in T are again routed on the newly designed network (revisednetwork) using R. This iterative process continues until all demands canbe successfully routed (step 312 determining if T′>0). The resultingnetwork is outputted, as denoted by block 314. Specification of theresulting network may take any one of a variety of forms known to thoseskilled in the art.

It can be verified that the resulting network can indeed carry all ofthe traffic in T in the same order because of the loop terminationcondition. Note that the cost of the network increases with eachiteration because new equipment gets added to carry the unroutabletraffic. However, the size of the failed traffic set may notmonotonically decrease from one iteration to the next. This is becausethe NMS may use the added capacity to route already routable demands andleave no path for the demands in the failed set. Despite this, it can beshown that methodology 300 is guaranteed to terminate in at most niterations, where n is the number of traffic demands in T. In practice,however, the number of iterations required may be much smaller than n.

The methodology of FIG. 3 may be made even more efficient such that thetraffic set is guaranteed to shrink with each iteration. In such anembodiment, at the end of each design, only the demands that wereconsidered in that design are routed using the NMS algorithm R (i.e.,the failed demand set and not all of T). This methodology is alsoexpected to terminate in n iterations. However, in the resultingnetwork, the demands in T are guaranteed to be routable if they arrivein the order they were successfully routed by the NMS in the designmethodology of the invention. This may be different from the originalorder in T.

Referring now to FIG. 4, a block diagram illustrates a generalizedhardware architecture of a computer system suitable for implementing anautomated design system, according to an embodiment of the presentinvention. More particularly, all or parts of design system 204 of FIG.2 (namely, planning engine 206 and NMS engine 208) may implement such acomputing system 400 to perform the techniques of the invention. Ofcourse, it is to be understood that the invention is not limited to anyparticular computing system implementation.

In this illustrative implementation, a processor 402 for implementing atleast a portion of the methodologies of the invention is operativelycoupled to a memory 404, input/output (I/O) device(s) 406 and a networkinterface 408 via a bus 410, or an alternative connection arrangement.It is to be appreciated that the term “processor” as used herein isintended to include any processing device, such as, for example, onethat includes a central processing unit (CPU) and/or other processingcircuitry (e.g., digital signal processor (DSP), microprocessor, etc.).Additionally, it is to be understood that the term “processor” may referto more than one processing device, and that various elements associatedwith a processing device may be shared by other processing devices.

The term “memory” as used herein is intended to include memory and othercomputer-readable media associated with a processor or CPU, such as, forexample, random access memory (RAM), read only memory (ROM), fixedstorage media (e.g., hard drive), removable storage media (e.g.,diskette), flash memory, etc.

In addition, the phrase “I/O devices” as used herein is intended toinclude one or more input devices (e.g., keyboard, mouse, etc.) forinputting data to the processing unit, as well as one or more outputdevices (e.g., CRT display, etc.) for providing results associated withthe processing unit. It is to be appreciated that such input devices maybe one mechanism for a user to provide the design inputs (e.g., 202 inFIG. 2) used by a design system (e.g., 204 in FIG. 2) to generate adesigned network (e.g., 210 in FIG. 2). Alternatively, the design inputscould be read into the design system from a diskette or from some othersource (e.g., another computer system) connected to the computer bus410. The output devices may be one mechanism for a user to be presentedwith a specification of the designed network. The I/O devices are also amechanism for a design system user to interact with the routingalgorithms, if the need or desire arises.

Still further, the phrase “network interface” as used herein is intendedto include, for example, one or more devices capable of allowing thecomputing system 400 to communicate with network equipment. Thus, thenetwork interface may comprise a transceiver configured to communicatewith a transceiver of piece of network equipment via a suitablecommunications protocol. It is to be understood that since thecommunications protocols employed may be specific to the types ofnetwork equipment, the invention is not limited to any particularcommunications protocol. It is further to be appreciated that the designsystem of the invention may communicate with network equipment via thenetwork interface when also implementing the design methodologiesdescribed in the U.S. patent application identified as Ser. No.10/426,500, entitled “Network Design Utilizing Integrated Planning andConfiguration,” filed concurrently herewith and commonly assigned, thedisclosure of which is incorporated by reference herein.

It is to be appreciated that while the present invention has beendescribed herein in the context of an automated design system, themethodologies of the present invention may be capable of beingdistributed in the form of computer readable media, and that the presentinvention may be implemented, and its advantages realized, regardless ofthe particular type of signal-bearing media actually used fordistribution. The term “computer readable media” as used herein isintended to include recordable-type media, such as, for example, afloppy disk, a hard disk drive, RAM, compact disk (CD) ROM, etc., andtransmission-type media, such as digital and analog communication links,wired or wireless communication links using transmission forms, such as,for example, radio frequency and optical transmissions, etc. Thecomputer readable media may take the form of coded formats that aredecoded for use in a particular data processing system.

Accordingly, one or more computer programs, or software componentsthereof, including instructions or code for performing the methodologiesof the invention, as described herein, may be stored in one or more ofthe associated storage media (e.g., ROM, fixed or removable storage)and, when ready to be utilized, loaded in whole or in part (e.g., intoRAM) and executed by the processor 402.

In any case, it is to be appreciated that the techniques of theinvention, described herein and shown in the appended figures, may beimplemented in various forms of hardware, software, or combinationsthereof, e.g., one or more operatively programmed general purposedigital computers with associated memory, implementation-specificintegrated circuit(s), functional circuitry, etc. Given the techniquesof the invention provided herein, one of ordinary skill in the art willbe able to contemplate other implementations of the techniques of theinvention.

Although illustrative embodiments of the present invention have beendescribed herein with reference to the accompanying drawings, it is tobe understood that the invention is not limited to those preciseembodiments, and that various other changes and modifications may bemade by one skilled in the art without departing from the scope orspirit of the invention.

1. An automated method of designing a network, the method comprising thesteps of: obtaining one or more traffic demands; computing a network,the network being computed by determining one or more routes for the oneor more traffic demands using a design-based routing methodology basedon feedback from a network management-based routing methodology; andoutputting the computed network as a designed network such that thedesigned network is implementable to process traffic.
 2. The method ofclaim 1, wherein the network computing step further comprises the stepsof: routing the one or more traffic demands using the design-basedrouting methodology to determine an initially-designed network topology;routing the one or more traffic demands on the initially-designednetwork topology using the network management-based routing methodologyto determine whether there is an unroutable traffic demand; and when anunroutable traffic demand has been determined, rerouting at least theunroutable traffic demand using the design-based routing methodology todetermine a revised network topology.
 3. The method of claim 2, whereinthe rerouting step further comprises rerouting all of the obtainedtraffic demands.
 4. The method of claim 2, further comprising the stepof rerouting the one or more traffic demands on the revised networktopology using the network management-based routing methodology.
 5. Themethod of claim 1, wherein the number of times the design-based routingmethodology and the network management-based routing methodology areused to route demands is equivalent to the number of traffic demands. 6.The method of claim 1, wherein the design-based routing methodologyroutes the one or more traffic demands based on one of cost and pathlength.
 7. The method of claim 1, wherein the network management-basedrouting methodology routes the one or more traffic demands based oncapacity.
 8. The method of claim 1, wherein the network being designedis an optical network.
 9. Apparatus for designing a network, theapparatus comprising: a memory; and at least one processor coupled tothe memory and operative to: (i) obtain one or more traffic demands;(ii) compute a network, the network being computed by determining one ormore routes for the one or more traffic demands using a design-basedrouting methodology based on feedback from a network management-basedrouting methodology; and (iii) output the computed network as a designednetwork such that the designed network is implementable to processtraffic.
 10. The apparatus of claim 9, wherein the network computingoperation further comprises: (i) routing the one or more traffic demandsusing the design-based routing methodology to determine aninitially-designed network topology; (ii) routing the one or moretraffic demands on the initially-designed network topology using thenetwork management-based routing methodology to determine whether thereis an unroutable traffic demand; and (iii) when an unroutable trafficdemand has been determined, rerouting at least the unroutable trafficdemand using the design-based routing methodology to determine a revisednetwork topology.
 11. The apparatus of claim 10, wherein the reroutingoperation further comprises rerouting all of the obtained trafficdemands.
 12. The apparatus of claim 10, wherein the at least oneprocessor is further operative to reroute the one or more trafficdemands on the revised network topology using the networkmanagement-based routing methodology.
 13. The apparatus of claim 9,wherein the number of times the design-based routing methodology and thenetwork management-based routing methodology are used to route demandsis equivalent to the number of traffic demands.
 14. The apparatus ofclaim 9, wherein the design-based routing methodology routes the one ormore traffic demands based on one of cost and path length.
 15. Theapparatus of claim 9, wherein the network management-based routingmethodology routes the one or more traffic demands based on capacity.16. The apparatus of claim 9, wherein the network being designed is anoptical network.
 17. Apparatus for designing a network, the apparatuscomprising: at least one computing system comprising a processor coupledto a memory, the at least one computing system being operative toimplement: a planning engine; and a network management engine, thenetwork management engine being coupled to the planning engine; whereinthe planning engine obtains one or more traffic demands and a network iscomputed by determining one or more routes for the one or more trafficdemands using a design-based routing methodology executed by theplanning engine based on feedback from a network management-basedrouting methodology executed by the network management engine; furtherwherein the computed network is outputted as a designed network suchthat the designed network is implementable to process traffic.